Result diversification has many important applications in databases,
operations research, information retrieval, and finance. In this paper, we
study and extend a particular version of result diversification, known as
max-sum diversification. More specifically, we consider the setting where we
are given a set of elements in a metric space and a set valuation function $f$
defined on every subset. For any given subset $S$, the overall objective is a
linear combination of $f(S)$ and the sum of the distances induced by $S$.
We consider mechanisms for utilitarian combinatorial allocation problems,
where agents are not assumed to be single-minded. This class of problems
includes combinatorial auctions, multi-unit auctions, unsplittable flow
problems, profit-maximizing scheduling, and others. We study the price of
anarchy for such mechanisms, which is a bound on the approximation ratio
obtained at any mixed Nash equilibrium. We demonstrate that a broad class of
greedy approximation algorithms can be implemented as mechanisms for which the
price of anarchy nearly matches the performance of the original algorithm.